Lung Cancer

Detailed Survival analyis of the Survival lung data.

Libraries

library(survival)
library(FRESA.CAD)

## Loading required package: Rcpp

## Loading required package: stringr

## Loading required package: miscTools

## Loading required package: Hmisc

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:base':
## 
##     format.pval, units

## Loading required package: pROC

## Type 'citation("pROC")' for a citation.

## 
## Attaching package: 'pROC'

## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var

op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('keep.trailing.zeros',TRUE)

Libraries

data(lung)

## Warning in data(lung): data set 'lung' not found

lung$inst <- NULL
lung$status <- lung$status - 1
lung <- lung[complete.cases(lung),]

pander::pander(table(lung$status))

0	1
47	121

pander::pander(summary(lung$time))

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
5	175	268	310	416	1022

Exploring Raw Features with RRPlot

convar <- colnames(lung)[lapply(apply(lung,2,unique),length) > 10]
convar <- convar[convar != "time"]
topvar <- univariate_BinEnsemble(lung[,c("status",convar)],"status")
pander::pander(topvar)

age	wt.loss
0.106	0.106

topv <- min(5,length(topvar))
topFive <- names(topvar)[1:topv]
RRanalysis <- list();
idx <- 1
for (topf in topFive)
{
  RRanalysis[[idx]] <- RRPlot(cbind(lung$status,lung[,topf]),
                              atRate=c(0.90),
                  timetoEvent=lung$time,
                  title=topf,
#                  plotRR=FALSE
                  )
  idx <- idx + 1
}

names(RRanalysis) <- topFive

Reporting the Metrics

ROCAUC <- NULL
CstatCI <- NULL
LogRangp <- NULL
Sensitivity <- NULL
Specificity <- NULL

for (topf in topFive)
{
  CstatCI <- rbind(CstatCI,RRanalysis[[topf]]$c.index$cstatCI)
  LogRangp <- rbind(LogRangp,RRanalysis[[topf]]$surdif$pvalue)
  Sensitivity <- rbind(Sensitivity,RRanalysis[[topf]]$ROCAnalysis$sensitivity)
  Specificity <- rbind(Specificity,RRanalysis[[topf]]$ROCAnalysis$specificity)
  ROCAUC <- rbind(ROCAUC,RRanalysis[[topf]]$ROCAnalysis$aucs)
}
rownames(CstatCI) <- topFive
rownames(LogRangp) <- topFive
rownames(Sensitivity) <- topFive
rownames(Specificity) <- topFive
rownames(ROCAUC) <- topFive

pander::pander(ROCAUC)

	est	lower	upper
age	0.589	0.491	0.686
wt.loss	0.560	0.462	0.659

pander::pander(CstatCI)

	mean.C Index	median	lower	upper
age	0.559	0.560	0.499	0.619
wt.loss	0.515	0.515	0.456	0.574

pander::pander(LogRangp)

age	0.919
wt.loss	0.358

pander::pander(Sensitivity)

	est	lower	upper
age	0.1157	0.0647	0.187
wt.loss	0.0496	0.0184	0.105

pander::pander(Specificity)

	est	lower	upper
age	0.872	0.743	0.952
wt.loss	0.894	0.769	0.965

meanMatrix <- cbind(ROCAUC[,1],CstatCI[,1],Sensitivity[,1],Specificity[,1])
colnames(meanMatrix) <- c("ROCAUC","C-Stat","Sen","Spe")
pander::pander(meanMatrix)

	ROCAUC	C-Stat	Sen	Spe
age	0.589	0.559	0.1157	0.872
wt.loss	0.560	0.515	0.0496	0.894

Modeling

ml <- BSWiMS.model(Surv(time,status)~1,data=lung,NumberofRepeats = 10)

[+++++++++++++++++++++++++++++]..

sm <- summary(ml)
pander::pander(sm$coefficients)

Table continues below

	Estimate	lower	HR	upper	u.Accuracy	r.Accuracy
ph.ecog	4.32e-01	1.194	1.541	1.988	0.679	0.649
sex	-4.59e-01	0.456	0.632	0.876	0.649	0.679
pat.karno	-1.77e-03	0.997	0.998	1.000	0.506	0.720
ph.karno	-4.64e-07	1.000	1.000	1.000	0.577	0.720
age	4.57e-08	1.000	1.000	1.000	0.565	0.720

Table continues below

	full.Accuracy	u.AUC	r.AUC	full.AUC	IDI	NRI
ph.ecog	0.601	0.601	0.620	0.600	0.0449	0.405
sex	0.601	0.620	0.601	0.600	0.0285	0.478
pat.karno	0.506	0.585	0.500	0.585	0.0292	0.342
ph.karno	0.577	0.570	0.500	0.570	0.0143	0.280
age	0.565	0.549	0.500	0.549	0.0162	0.195

	z.IDI	z.NRI	Delta.AUC	Frequency
ph.ecog	3.33	2.48	-0.02005	1.0
sex	2.76	2.85	-0.00167	1.0
pat.karno	2.44	2.24	0.08546	1.0
ph.karno	2.22	1.64	0.06998	0.8
age	1.97	1.14	0.04871	0.1

Cox Model Performance

Here we evaluate the model using the RRPlot() function.

The evaluation of the raw Cox model with RRPlot()

Here we will use the predicted event probability assuming a baseline hazard for events withing 5 years

timeinterval <- 2*mean(subset(lung,status==1)$time)

h0 <- sum(lung$status & lung$time <= timeinterval)
h0 <- h0/sum((lung$time > timeinterval) | (lung$status==1))
pander::pander(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")

Initial Parameters

h0	timeinterval
0.85	578

index <- predict(ml,lung)

rdata <- cbind(lung$status,ppoisGzero(index,h0))

rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Raw Train: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

By Risk Categories

obsexp <- rrAnalysisTrain$OERatio$atThrEstimates
pander::pander(obsexp)

	Observed	L.CI	H.CI	Expected	O/E	Low	Upper	pvalue
Total	121	100.4	144.6	77.3	1.57	1.30	1.87	3.70e-06
low	83	66.1	102.9	51.6	1.61	1.28	1.99	4.97e-05
90%	38	26.9	52.2	25.9	1.47	1.04	2.01	2.33e-02

maxx <- 1.1*max(c(obsexp$Observed,obsexp$Expected))
minx <- 0.9*min(c(obsexp$Observed,obsexp$Expected))

plot(obsexp$Expected,obsexp$Observed,
     xlim=c(minx,maxx),
     ylim=c(minx,maxx),
     main="Cal. Expected vs Observed",
     ylab="Observed",
     xlab="Expected",
     col=rainbow(nrow(obsexp)),
     log="xy")

errbar(obsexp$Expected,obsexp$Observed,obsexp$L.CI,obsexp$H.CI,add=TRUE,pch=0,errbar.col=rainbow(nrow(obsexp)),cex=0.75)
lines(x=c(1,maxx),y=c(1,maxx),lty=2)
text(obsexp$Expected,obsexp$Observed,rownames(obsexp),pos=2,cex=0.75)

Time to Event Analysis

isevent <- rrAnalysisTrain$timetoEventData$eStatus == 1
exptime <- boxplot(rrAnalysisTrain$timetoEventData$expectedTime[isevent]~rrAnalysisTrain$timetoEventData$class[isevent],plot=FALSE)
obstime <- boxplot(rrAnalysisTrain$timetoEventData$eTime[isevent]~rrAnalysisTrain$timetoEventData$class[isevent],plot=FALSE)
classnames <- attr(rrAnalysisTrain$timetoEventData,"ClassNames") 
timesdata <- cbind(obstime$stats[c(2,3,4),],exptime$stats[c(2,3,4),])
rownames(timesdata) <- c("1Q","Median","3Q")
colnames(timesdata) <- c(paste("O",classnames,sep=":"),paste("E",classnames,sep=":"))
pander::pander(timesdata)

	O:Low	O:At Risk > 0.648	E:Low	E:At Risk > 0.648
1Q	168	92	286	176
Median	285	180	433	181
3Q	431	329	447	271

minv <- min(c(exptime$stats[2,],obstime$stats[2,]))
if (minv<0.001) minv <-0.001
maxv <- max(c(exptime$stats[4,],obstime$stats[4,]))
errbar(exptime$stats[3,],obstime$stats[3,],obstime$stats[2,],obstime$stats[4,],log="xy",
     xlab="Mean Expected Time",
     ylab="Mean Observed",
     xlim=c(minv,maxv),
     ylim=c(minv,maxv),
     main="Cal. Estimated Time to Event",col=rainbow(length(classnames)))

lines(x=c(minv,maxv),y=c(minv,maxv),col="black",lty=2)
legend("topleft",legend=classnames,lty=c(0),pch=c(16),col=rainbow(length(classnames)),cex=0.80)

Uncalibrated Performance Report

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")

Threshold values

	@:0.9	@MAX_BACC	@MAX_RR	@SPE100	p(0.5)
Thr	0.649	0.478	3.39e-01	3.39e-01	0.493
RR	1.214	1.742	6.85e+01	6.85e+01	1.270
RR_LCI	1.014	1.260	1.44e-01	1.44e-01	1.037
RR_UCI	1.454	2.408	3.26e+04	3.26e+04	1.555
SEN	0.314	0.826	1.00e+00	1.00e+00	0.612
SPE	0.830	0.511	1.91e-01	1.91e-01	0.596
BACC	0.572	0.669	5.96e-01	5.96e-01	0.604
NetBenefit	0.138	0.470	6.04e-01	6.04e-01	0.331

pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")

O/E Ratio

O/E	Low	Upper	p.value
1.57	1.3	1.87	3.7e-06

pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")

O/E Mean

mean	50%	2.5%	97.5%
1.23	1.23	1.19	1.27

pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")

O/Acum Mean

mean	50%	2.5%	97.5%
1.27	1.27	1.26	1.29

pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")

mean.C Index	median	lower	upper
0.651	0.653	0.595	0.711

pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")

ROC AUC

est	lower	upper
0.691	0.598	0.784

pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")

Sensitivity

est	lower	upper
0.314	0.233	0.405

pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")

Specificity

est	lower	upper
0.83	0.692	0.924

pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")

Probability Thresholds

90%
0.648

pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")

Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821

	N	Observed	Expected	(O-E)^2/E	(O-E)^2/V
class=0	122	83	95.6	1.65	7.95
class=1	46	38	25.4	6.22	7.95

Cox Calibration

op <- par(no.readonly = TRUE)


calprob <- CoxRiskCalibration(ml,lung,"status","time")

( 849.0437 , 781.9918 , 1.468556 , 121 , 140.6965 )

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")

h0	Gain	DeltaTime
1.55	1.83	849

The RRplot() of the calibrated model

h0 <- calprob$h0
timeinterval <- calprob$timeInterval;

rdata <- cbind(lung$status,calprob$prob)


rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Train Cal: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

By Risk Categories

obsexp <- rrAnalysisTrain$OERatio$atThrEstimates
pander::pander(obsexp)

	Observed	L.CI	H.CI	Expected	O/E	Low	Upper	pvalue
Total	121	100.4	144.6	96.2	1.26	1.044	1.50	0.0142
low	83	66.1	102.9	64.2	1.29	1.029	1.60	0.0244
90%	38	26.9	52.2	32.3	1.18	0.833	1.62	0.2912

maxx <- 1.1*max(c(obsexp$Observed,obsexp$Expected))
minx <- 0.9*min(c(obsexp$Observed,obsexp$Expected))

plot(obsexp$Expected,obsexp$Observed,
     xlim=c(minx,maxx),
     ylim=c(minx,maxx),
     main="Cal. Expected vs Observed",
     ylab="Observed",
     xlab="Expected",
     col=rainbow(nrow(obsexp)),
     log="xy")

errbar(obsexp$Expected,obsexp$Observed,obsexp$L.CI,obsexp$H.CI,add=TRUE,pch=0,errbar.col=rainbow(nrow(obsexp)),cex=0.75)
lines(x=c(1,maxx),y=c(1,maxx),lty=2)
text(obsexp$Expected,obsexp$Observed,rownames(obsexp),pos=2,cex=0.75)

Time to Event Analysis

isevent <- rrAnalysisTrain$timetoEventData$eStatus == 1
exptime <- boxplot(rrAnalysisTrain$timetoEventData$expectedTime[isevent]~rrAnalysisTrain$timetoEventData$class[isevent],plot=FALSE)
obstime <- boxplot(rrAnalysisTrain$timetoEventData$eTime[isevent]~rrAnalysisTrain$timetoEventData$class[isevent],plot=FALSE)
classnames <- attr(rrAnalysisTrain$timetoEventData,"ClassNames") 
timesdata <- cbind(obstime$stats[c(2,3,4),],exptime$stats[c(2,3,4),])
rownames(timesdata) <- c("1Q","Median","3Q")
colnames(timesdata) <- c(paste("O",classnames,sep=":"),paste("E",classnames,sep=":"))
pander::pander(timesdata)

	O:Low	O:At Risk > 0.851	E:Low	E:At Risk > 0.851
1Q	168	92	230	142
Median	285	180	348	145
3Q	431	329	359	218

minv <- min(c(exptime$stats[2,],obstime$stats[2,]))
if (minv<0.001) minv <-0.001
maxv <- max(c(exptime$stats[4,],obstime$stats[4,]))
errbar(exptime$stats[3,],obstime$stats[3,],obstime$stats[2,],obstime$stats[4,],log="xy",
     xlab="Mean Expected Time",
     ylab="Mean Observed",
     xlim=c(minv,maxv),
     ylim=c(minv,maxv),
     main="Cal. Estimated Time to Event",col=rainbow(length(classnames)))

lines(x=c(minv,maxv),y=c(minv,maxv),col="black",lty=2)
legend("topleft",legend=classnames,lty=c(0),pch=c(16),col=rainbow(length(classnames)),cex=0.80)

Calibrated Train Performance

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")

Threshold values

	@:0.9	@MAX_BACC	@MAX_RR	@SPE100	p(0.5)
Thr	0.8523	0.695	5.31e-01	5.31e-01	5.31e-01
RR	1.2142	1.742	6.85e+01	6.85e+01	6.85e+01
RR_LCI	1.0143	1.260	1.44e-01	1.44e-01	1.44e-01
RR_UCI	1.4536	2.408	3.26e+04	3.26e+04	3.26e+04
SEN	0.3140	0.826	1.00e+00	1.00e+00	1.00e+00
SPE	0.8298	0.511	1.91e-01	1.91e-01	1.91e-01
BACC	0.5719	0.669	5.96e-01	5.96e-01	5.96e-01
NetBenefit	-0.0484	0.283	4.64e-01	4.64e-01	4.64e-01

pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")

O/E Ratio

O/E	Low	Upper	p.value
1.26	1.04	1.5	0.0142

pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")

O/E Mean

mean	50%	2.5%	97.5%
0.99	0.989	0.955	1.02

pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")

O/Acum Mean

mean	50%	2.5%	97.5%
1.02	1.02	1.01	1.02

pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")

mean.C Index	median	lower	upper
0.651	0.65	0.591	0.711

pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")

ROC AUC

est	lower	upper
0.691	0.598	0.784

pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")

Sensitivity

est	lower	upper
0.314	0.233	0.405

pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")

Specificity

est	lower	upper
0.83	0.692	0.924

pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")

Probability Thresholds

90%
0.851

pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")

Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821

	N	Observed	Expected	(O-E)^2/E	(O-E)^2/V
class=0	122	83	95.6	1.65	7.95
class=1	46	38	25.4	6.22	7.95

Cross-Validation

rcv <- randomCV(theData=lung,
                theOutcome = Surv(time,status)~1,
                fittingFunction=BSWiMS.model, 
                trainFraction = 0.95,
                repetitions=200,
                classSamplingType = "Pro"
         )

.[++].[+++].[+++].[+++].[++].[++].[+].[+++].[+++].[+++]10 Tested: 74 Avg. Selected: 3.5 Min Tests: 1 Max Tests: 4 Mean Tests: 1.351351 . MAD: 0.4864521

.[+++].[+++].[+].[++].[+++].[+++].[+].[++].[+++].[+]20 Tested: 118 Avg. Selected: 3.35 Min Tests: 1 Max Tests: 5 Mean Tests: 1.694915 . MAD: 0.4734697

.[+++].[+++].[++].[++].[+].[++].[++].[+++].[++++].[+++]30 Tested: 144 Avg. Selected: 3.4 Min Tests: 1 Max Tests: 5 Mean Tests: 2.083333 . MAD: 0.4749689

.[+++].[+++].[+++].[+++].[++].[++].[++].[+++].[+++].[+++]40 Tested: 154 Avg. Selected: 3.475 Min Tests: 1 Max Tests: 7 Mean Tests: 2.597403 . MAD: 0.4785025

.[+++].[++].[+++].[+].[+++].[++].[+].[+++].[+++].[++]50 Tested: 159 Avg. Selected: 3.44 Min Tests: 1 Max Tests: 8 Mean Tests: 3.144654 . MAD: 0.4783724

.[++].[++-].[++++].[+].[++].[+++].[+++].[++].[+++].[+++]60 Tested: 165 Avg. Selected: 3.45 Min Tests: 1 Max Tests: 9 Mean Tests: 3.636364 . MAD: 0.4777576

.[++].[++].[+++].[++].[+].[+++].[+++].[+++].[++++].[++++]70 Tested: 165 Avg. Selected: 3.471429 Min Tests: 1 Max Tests: 9 Mean Tests: 4.242424 . MAD: 0.4783279

.[++++].[++++].[+++].[+++].[++-].[++++].[+++].[+++].[+++].[++-]80 Tested: 165 Avg. Selected: 3.55 Min Tests: 1 Max Tests: 10 Mean Tests: 4.848485 . MAD: 0.4782539

.[+++].[++].[+++].[+++].[+++].[++].[+].[+].[+].[+++]90 Tested: 166 Avg. Selected: 3.511111 Min Tests: 1 Max Tests: 11 Mean Tests: 5.421687 . MAD: 0.4781686

.[++++].[++].[+++].[+++].[++++].[+++].[++++].[+++].[++].[+]100 Tested: 167 Avg. Selected: 3.55 Min Tests: 1 Max Tests: 12 Mean Tests: 5.988024 . MAD: 0.4766993

.[++].[+].[+++].[+++].[+++].[+++].[+++].[++].[++++].[++]110 Tested: 168 Avg. Selected: 3.554545 Min Tests: 1 Max Tests: 14 Mean Tests: 6.547619 . MAD: 0.476821

.[++].[++].[++].[++].[+++].[++].[+].[+++].[+].[++]120 Tested: 168 Avg. Selected: 3.508333 Min Tests: 1 Max Tests: 14 Mean Tests: 7.142857 . MAD: 0.4766714

.[+++].[+++].[+++].[+++].[+++].[++].[++].[++].[+].[+++]130 Tested: 168 Avg. Selected: 3.507692 Min Tests: 1 Max Tests: 15 Mean Tests: 7.738095 . MAD: 0.4760375

.[+++].[++-].[++].[++].[+].[++].[+++].[+++].[++].[++]140 Tested: 168 Avg. Selected: 3.485714 Min Tests: 1 Max Tests: 16 Mean Tests: 8.333333 . MAD: 0.4760829

.[+++].[+].[+++].[+++].[+].[+++].[+++].[+++].[++].[+++]150 Tested: 168 Avg. Selected: 3.486667 Min Tests: 2 Max Tests: 16 Mean Tests: 8.928571 . MAD: 0.4764672

.[+++].[++].[++].[+].[+].[++].[+].[+++].[+++].[++]160 Tested: 168 Avg. Selected: 3.45625 Min Tests: 2 Max Tests: 19 Mean Tests: 9.52381 . MAD: 0.476116

.[+++].[+++].[++].[+++].[++].[++].[++].[+++].[++].[+++]170 Tested: 168 Avg. Selected: 3.458824 Min Tests: 2 Max Tests: 20 Mean Tests: 10.11905 . MAD: 0.476003

.[++-].[+++].[++].[++].[+++].[+].[+++].[+++].[+++].[++++]180 Tested: 168 Avg. Selected: 3.466667 Min Tests: 2 Max Tests: 20 Mean Tests: 10.71429 . MAD: 0.4755427

.[+++].[++].[+].[+++].[++].[++].[+++].[++].[+++].[++]190 Tested: 168 Avg. Selected: 3.457895 Min Tests: 3 Max Tests: 21 Mean Tests: 11.30952 . MAD: 0.4754696

.[++].[++-].[++-].[++].[+++].[+++].[+++].[+++].[+++].[++-]200 Tested: 168 Avg. Selected: 3.46 Min Tests: 3 Max Tests: 23 Mean Tests: 11.90476 . MAD: 0.4756583

stp <- rcv$survTestPredictions
stp <- stp[!is.na(stp[,4]),]

bbx <- boxplot(unlist(stp[,1])~rownames(stp),plot=FALSE)
times <- bbx$stats[3,]
status <- boxplot(unlist(stp[,2])~rownames(stp),plot=FALSE)$stats[3,]
prob <- ppoisGzero(boxplot(unlist(stp[,4])~rownames(stp),plot=FALSE)$stats[3,],h0)

rdatacv <- cbind(status,prob)
rownames(rdatacv) <- bbx$names
names(times) <- bbx$names

rrAnalysisTest <- RRPlot(rdatacv,atRate=c(0.90),
                     timetoEvent=times,
                     title="Test: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Cross-Validation Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")

Threshold values

	@:0.9	@MAX_BACC	@MAX_RR	@SPE100	p(0.5)
Thr	0.856	0.667	0.667	0.493	0.504
RR	1.186	2.958	2.958	1.000	0.719
RR_LCI	0.972	1.387	1.387	0.000	0.653
RR_UCI	1.447	6.309	6.309	0.000	0.790
SEN	0.198	0.959	0.959	1.000	0.992
SPE	0.894	0.298	0.298	0.000	0.000
BACC	0.546	0.628	0.628	0.500	0.496
NetBenefit	-0.034	0.297	0.297	0.448	0.430

pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")

O/E Ratio

O/E	Low	Upper	p.value
1.23	1.02	1.48	0.0229

pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")

O/E Mean

mean	50%	2.5%	97.5%
0.978	0.978	0.946	1.01

pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")

O/Acum Mean

mean	50%	2.5%	97.5%
0.969	0.968	0.958	0.979

pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")

mean.C Index	median	lower	upper
0.593	0.59	0.522	0.651

pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")

ROC AUC

est	lower	upper
0.599	0.497	0.702

pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")

Sensitivity

est	lower	upper
0.19	0.124	0.271

pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")

Specificity

est	lower	upper
0.894	0.769	0.965

pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")

Probability Thresholds

90%
0.86

pander::pander(rrAnalysisTest$surdif,caption="Logrank test")

Logrank test Chisq = 2.346444 on 1 degrees of freedom, p = 0.125569

	N	Observed	Expected	(O-E)^2/E	(O-E)^2/V
class=0	140	98	103.8	0.328	2.35
class=1	28	23	17.2	1.981	2.35

Calibrating the test results

rdatacv <- cbind(status,prob,times)
calprob <- CalibrationProbPoissonRisk(rdatacv)

( 858.7891 , 781.9918 , 1.485412 , 121 , 143.3832 )

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")

h0	Gain	DeltaTime
0.85	1	859

timeinterval <- calprob$timeInterval;

rdata <- cbind(status,calprob$prob)


rrAnalysisTest <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=times,
                     title="Calibrated Test: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Calibrated Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")

Threshold values

	@:0.9	@MAX_BACC	@MAX_RR	@SPE100	p(0.5)
Thr	0.856	0.667	0.667	0.493	0.504
RR	1.186	2.958	2.958	1.000	0.719
RR_LCI	0.972	1.387	1.387	0.000	0.653
RR_UCI	1.447	6.309	6.309	0.000	0.790
SEN	0.198	0.959	0.959	1.000	0.992
SPE	0.894	0.298	0.298	0.000	0.000
BACC	0.546	0.628	0.628	0.500	0.496
NetBenefit	-0.034	0.297	0.297	0.448	0.430

pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")

O/E Ratio

O/E	Low	Upper	p.value
1.25	1.04	1.49	0.0169

pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")

O/E Mean

mean	50%	2.5%	97.5%
0.989	0.99	0.956	1.02

pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")

O/Acum Mean

mean	50%	2.5%	97.5%
0.969	0.97	0.958	0.98

pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")

mean.C Index	median	lower	upper
0.593	0.594	0.531	0.659

pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")

ROC AUC

est	lower	upper
0.599	0.497	0.702

pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")

Sensitivity

est	lower	upper
0.19	0.124	0.271

pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")

Specificity

est	lower	upper
0.894	0.769	0.965

pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")

Probability Thresholds

90%
0.86

pander::pander(rrAnalysisTest$surdif,caption="Logrank test")

Logrank test Chisq = 2.346444 on 1 degrees of freedom, p = 0.125569

	N	Observed	Expected	(O-E)^2/E	(O-E)^2/V
class=0	140	98	103.8	0.328	2.35
class=1	28	23	17.2	1.981	2.35